Everything to Know About Stratified Sampling

Stratified sampling is a way to study a sample that best represents your whole population. In product and web experiments, this “population” is usually your users or customers.

Instead of randomly picking people from all your users, you split them into groups based on something they have in common (shared characteristics). These groups are called strata.

You then randomly select people from each group. This method ensures that even smaller subgroups are included in your study.

Using stratified sampling helps you capture insights from all parts of your user base, not just the most common areas. Experiments with this technique lead to more reliable data and help you make smarter decisions about your product. It’s like hearing from everyone at the party, not just the loudest guests.

Key features

To understand stratified sampling, you must first know a few important terms and features.

Population division

In stratified sampling, the whole user base is divided into different subgroups or strata. For example, you might split users into “free,” “premium,” and “enterprise” tiers.

Similar individuals

Each stratum has individuals with similar characteristics. For instance, all “premium” users can access the same features.

Different strata

The strata are distinctly different from each other. For example, “free” users have different usage patterns than “enterprise” users.

Random selection

Participants are chosen randomly within each stratum. You might use a random number generator to help you pick specific premium users for your study.

The process

Stratified sampling usually follows these steps:

1. Define the population: Clearly identify the group you want to study.
2. Choose the stratification variable(s): Decide which characteristics will divide your population, such as age, gender, location, or user type.
3. Divide the population into strata: Create subgroups based on your chosen variables.
4. : Decide how many participants you need for impactful results.
5. Allocate sample sizes to each stratum: Your sample sizes can be proportional to the size of each stratum in the population or tweaked (i.e., disproportionate) to suit your research goals.
6. Randomly select participants: Choose people randomly from each stratum to form your final sample—this is the group you’ll experiment with.

This process helps you create a miniature version of your entire user base, showing its full diversity. Your experiment results will accurately reflect the needs and behaviors of all your users, not just a few.

Understanding how different groups interact with your product can help you decide what features to release or improve.

Proportional vs. disproportionate stratified sampling

Stratified sampling usually takes two main approaches: proportional and disproportionate.

Proportional stratified sampling

Proportional stratified sampling is the most common technique used in experiments. Here, the proportion of each stratum in your sample matches its proportion in the overall population.

For example, if 70% of your users are on a free plan, 20% on the basic paid plan, and 10% on the premium plan, your sample would reflect these percentages. When testing a new feature that affects all user types equally, proportional sampling gives you a clear picture of the overall impact.

Using a proportional technique provides a balanced representation of the user base and makes your results easier to interpret and generalize. However, smaller strata may need more representatives for meaningful analysis.

Disproportionate stratified sampling

This approach intentionally over or under-samples certain strata to achieve specific testing goals.

For instance, you might choose equal numbers from each user plan in your sample, regardless of their proportions in your user base. Or, to investigate the impact of a premium feature, you might oversample premium users to get more detailed insights about their experience.

Disproportionate stratified sampling can help you analyze smaller but important . The technique is also useful when certain groups are of a particular interest.

Product teams might find a disproportionate approach more complex to set up and interpret. You’ll also need to apply weights to the analysis to ensure it represents the true population.

Choosing a sampling method is one of the first steps when designing your product experiment.

Stratified sampling is usually compared to two other sampling methods: simple random sampling and cluster sampling.

Simple random sampling

Imagine you have a list of all your users. Simple random sampling would use a random number generator to select users from this list without considering other criteria—like drawing names out of a hat.

Despite its simplicity, this approach has advantages:

• Unbiased: Every user has an equal chance of being selected.
• Easy to implement: Most statistical tools support this method, and it requires minimal knowledge about the user base.

For example, you might use simple random sampling when testing a new homepage design. This approach works well if your users interact similarly with your homepage, such as clicking the same buttons or links or spending an equal amount of time on the page.

However, simple random sampling has some downsides:

• Underrepresentation: Minority user groups might be missed if you have a small but important segment (such as high-value customers).
• Inefficiency for varied populations: For very diverse populations, you might need to use a large sample size to ensure you represent all user types.

Cluster sampling

Cluster sampling usually involves grouping users by geographic region, time zone, or other natural groupings. You would then sample that entire group.

Cluster sampling can be advantageous:

• Cost-effective: It’s cheaper when individual sampling is expensive or difficult.
• Useful for geographically focused studies: Ideal for studies involving specific locations or natural user groupings.

You might use cluster sampling to test localization features, selecting users from different countries or language groups.

However, cluster sampling has limitations:

• Less precision: It’s less precise than stratified sampling. If clusters are internally similar but different, you might miss important variations.
• Potential bias: If clusters are not representative of the whole population, this method can introduce bias.

Stratified sampling benefits businesses with varied user needs and intricate .

The statistical technique helps product teams:

• Capture the full spectrum of user experiences
• Uncover nuanced insights that might be missed with other sampling methods
• Show how different user segments respond to changes
• Tailor experiences to different user groups
• Make more informed, inclusive product decisions

By representing all user groups, you avoid focusing only on the most common or vocal users. This approach leads to creating products that genuinely serve everyone.

Common applications

Stratified sampling is helpful in many types of experiments. Here are some of the most common ways you can use the technique.

Testing a new feature

Say you’re adding a new feature to your product and want to of all ages. With stratified sampling, you can test the software with a balanced mix of younger and older users to see how well it performs across different age groups.

Picking the best design

During a website or software update, use stratified sampling to gather feedback from new users and regular visitors. This method will help you understand how each group responds to the design changes.

Trying out different prices

To for your product, use stratified sampling to test various price points with customers from different cities or budget ranges. Then, using the data, find a price that drives the most sales for each segment.

Improving customer support

When enhancing your help desk, stratified sampling lets you hear from frequent and infrequent users. You can make changes knowing you’ve captured feedback from all perspectives on customer support.

Releasing a new product

Before , use stratified sampling to test it with various customer types—such as beginners and experts—to see how each group reacts. The insights may indicate if you need to make any pre-release tweaks.

Almost every industry and product type can use stratified sampling. It’s a versatile and creative tool that helps address each field’s unique needs and challenges.

Let’s explore how you might use the method across different sectors.

Ecommerce brands

• Optimize by stratifying customers based on their lifetime value. How do the changes impact their buying decisions?

• Fine-tune product recommendations by analyzing strata. Which user groups show the most significant shift in their buying behavior?

• Evaluate pricing strategies across different geographic regions or customer segments. Does pricing impact sales differently based on customer location?

SaaS products

• Enhance onboarding flows by considering different users' technical expertise. Do less tech-savvy users find the process more accessible and useful?

• Tailor features for by stratifying based on company size. Do smaller and larger companies have distinct feature needs or usage patterns?

• Analyze user retention rates across different subscription tiers. Which subscription tier shows the highest and lowest ?

Content platforms

• Refine recommendation algorithms using viewer interest categories. How do changes in recommendations impact user engagement across different interest groups?

• Evaluate subscription models through user engagement levels. Do price adjustments influence activity levels for highly engaged versus less active users?

• Improve your by stratifying based on user demographics or viewing habits. Which segment shows the most interest in a specific content type?

Healthcare software

• Assess treatment efficacy across diverse patient demographics. Do certain groups experience different levels of treatment effectiveness?

• Improve patient experience by stratifying based on service usage frequency. How does frequent service use correlate with patient satisfaction?

• Analyze telemedicine adoption rates among different age groups or conditions. Which segments show higher or lower adoption?

Banks and financial services

• Segment customers according to account types or investment levels to boost product adoption rates. Which customers are more likely to adopt new products?

• Gauge customer satisfaction through stratification by tenure or transaction volume. Does this metric vary significantly based on how longstanding and active the customer is?

• Evaluate risk assessment models by stratifying clients based on credit score or financial behaviors. How does the accuracy of the model differ across strata?

Using stratified sampling in experiments means incorporating it at every stage, from designing your experiment to implementing it and analyzing the results.

This approach gives you a more complete and representative view of your user base, leading to more reliable insights and better decision-making.

Here’s a quick guide to help you through the process.

Design

The design phase is crucial for setting up a successful stratified sampling experiment.

Start by outlining what you want to learn from your experiment and choosing the traits that will form your strata. These characteristics should be relevant to your research question and likely to impact .

For example, if you’re testing a new feature in a productivity app, you might stratify users by subscription tier (free, pro, or enterprise) and usage frequency (daily, weekly, or monthly).

Next, decide on the total number of participants you need and how to allocate this number across your strata. You can use proportional allocation to reflect your user base or disproportionate allocation to ensure adequate representation for each group (whatever its size).

Carry out

Once your design is set, it’s time to set your plan into action.

Use your product’s analysis system or user database to divide users according to your defined strata.

Create a system to select users from each stratum randomly. Many tools and survey platforms offer built-in features for stratified sampling. If not, you should combine database queries with randomization scripts.

Ensure your methods capture not only the results of your experiment but also the stratum to which each participant belongs. This information is crucial for later analysis.

Check to ensure your sample matches your design throughout the testing phase. Be prepared to adjust if certain strata are underrepresented due to low response rates or technical issues.

Analyze and interpret

Start by looking at the results from each stratum individually. Identify any patterns or trends specific to each group. For example, frequent users react differently to a new feature than casual users.

After analyzing each stratum, merge the results to get an overall picture. If you used disproportionate sampling, apply the appropriate weights to each stratum to ensure your final results accurately reflect your user population.

Compare the outcomes of your stratified sampling with what you would have seen using simple random sampling. This comparison can highlight missed insights without stratification.

Relate your findings back to your original research question. Consider how differences between strata might influence your product decisions or suggest new areas for research.

Imagine you want to test a new onboarding flow for your project management tool to improve user activation rates.

First, you need to identify key strata. After analyzing your customer data, you decide to stratify your sample based on two characteristics:

• Company size: Small (1-50 employees), medium (51-500 employees), and enterprise (500+ employees)
• Subscription tier: Basic, professional, and enterprise

You decide to test with 3,000 new account signups—this is your sample size.

Choosing a disproportionate allocation ensures you’ll get an adequate representation of larger clients:

• 40% small, 40% medium, and 20% enterprise companies
• 50% basic, 30% professional, and 20% enterprise tier

You randomly select new signups from each stratum to match these proportions. For example:

• 600 small companies on the basic tier (20% of the total sample)
• 360 medium companies on the professional tier (12% of the total sample)
• 120 enterprise companies on the enterprise tier (4% of the total sample)

Half of each stratum experiences the new onboarding process (treatment group), while the other half goes through the current process (control group).

After running the test for one month, you analyze the results:

• Overall, the new onboarding increased 30-day activation rates by 20%
• Small companies showed a 30% increase in activation rates
• Enterprise-tier customers had only a 5% increase in activation
• Medium-sized companies in the professional tier showed the greatest improvement at 35%

Using stratified sampling, you’ve gained insights into the overall effectiveness of the new onboarding process and how it impacted different customer segments. This data enables you to make more nuanced decisions, such as:

• Further improving the onboarding for small and medium-sized companies
• Investigating why enterprise customers didn’t see as much benefit
• Considering a tailored onboarding experience for different company sizes and subscription tiers

This approach ensures all your customer types are represented in your test, leading to more reliable and actionable results than if you had used simple random sampling. Stratified sampling helps you avoid over-indexing on your most numerous customers (small businesses) while gaining valuable insights about your higher-value enterprise clients.

While stratified sampling has many benefits, it also comes with some challenges. Knowing these pitfalls can help you plan better and run stronger experiments.

• Choosing the right strata: Picking irrelevant stratification characteristics can complicate your analysis without adding value. Regularly review and refine your criteria based on what you find and your changing product goals.

• Maintaining sample sizes: Some strata might have very few users, making it hard to draw conclusions. Be willing to change your approach or combine similar strata if necessary.

• Handling overlaps: Users may fit into multiple categories (e.g., an active user on a free plan). Decide in advance how to handle these overlaps to keep your categorization consistent.

• Balancing precision and complexity: More strata can lead to more precise results, but they make the experiment and analysis more complex. Find a balance that provides valuable insights without overcomplicating things.

• Ensuring data privacy: Stratification often requires more detailed user data. Always follow regulations and ethical guidelines when collecting and using this information.

To get the most out of stratified sampling, follow these best practices. They will help you design more effective studies, overcome hurdles, and extract valuable insights from your data.

Choose relevant stratification variables

Select characteristics relevant to your research and those likely to impact your results. For example, when testing a new feature in a mobile app, factors such as device type or operating system are more relevant than user age.

Ensure mutually exclusive and exhaustive strata

Make sure each user fits into only one category within each stratification variable. For instance, when dividing users by usage frequency, clearly define “low,” “medium,” and “high” usage to avoid overlap.

Balance sample sizes

While proportional allocation often works well, some strata may need larger samples to ensure reliable results. Consider oversampling smaller but more important user segments to gather enough data for meaningful conclusions.

Use pilot studies

Conduct a small pilot study before full experiments. This study helps validate your stratification approach, spot any issues with data collection, and refine your analysis methods.

Stay consistent

Apply your stratification criteria consistently across other related experiments. This consistency allows for better comparisons between studies and helps build a clearer picture of your user base over time.

Document your methods

Clearly record your stratification criteria, sampling methods, and analysis techniques. Documentation ensures transparency, enables replication of your studies, and helps your team understand and build upon your work.

Consider dynamic stratification

For long-running experiments or products with rapidly changing user bases, periodically reassess and adjust your strata to keep them relevant and representative.

Combine with other techniques

Don’t be afraid to mix stratified sampling with other methods when applicable. For example, cluster sampling within strata can be used for geographically diverse user groups.

Account for non-responses

In experiments requiring active participation, be ready for varying response rates across strata. Have a plan to handle non-response bias, such as oversampling from groups with lower response rates.

Iterate and improve

After each study, review how effective your stratification approach was. Did the strata make sense? Did they provide useful insights? Use what you learn to refine your approach for future experiments.

Having the right tools is everything when it comes to experiments. helps you seamlessly integrate structured sampling into your product development process and gain deeper insights into user behavior.

• The platform’s advanced segmentation enables you to easily create strata based on user characteristics, behaviors, or any custom properties you track. Implement stratified sampling without the hassle of a manual setup.

• Real-time targeting ensures your experiments always reflect your current user base, even as it evolves. Keep your data relevant and up-to-date.

• Dive deep into how different user segments respond to your experiments with . Compare results across strata and uncover nuanced insights.

• Remove the guesswork from interpreting results with advanced statistical analysis tools that account for your stratified sampling design.

• Communicate your findings via intuitive and customizable reports, providing solid data to support your product decisions.

Move beyond simply collecting data—use it to truly understand your users. Turn figures into clear, actionable insights that help drive your product forward.